{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import csv\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "HdoEaY30UXV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#download data from google search with a specific file type\n",
        "#approach 2\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "import spacy\n",
        "import re\n",
        "from googlesearch import search\n",
        "#Looking at the chart for 2019 year, what percentage of the primary energy is wasted (i.e. rejected) in each of the following categories? Report your answers to the nearest integer.\n",
        "text = input(\"Your question is : \")\n",
        "#selected = re.findall(r'\\b(petroleum|transportation|industrial uses)\\b', text, re.IGNORECASE)\n",
        "#print(\"selected:\", selected)\n",
        "numbers = re.findall(r'\\d+\\.?\\d*', text)\n",
        "print(\"Numbers:\", numbers)\n",
        "units =re.findall(r'\\b\\d+\\.?\\d*\\s?(liters|tons|kg|g|ml|cm|m|km|inch|ft|quads|year)\\b', text)\n",
        "print(\"Units:\", units)\n",
        "\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "doc = nlp(text)\n",
        "\n",
        "keywords = [token.text for token in doc if token.pos_ in (\"NOUN\")]\n",
        "print(\"Keywords:\", keywords)\n",
        "keywords = ['data', 'quads', 'petroleum', 'quads', 'transportation', 'quads', 'uses']\n",
        "\n",
        "#combine them\n",
        "combined=numbers+units+keywords\n",
        "result = \"+\".join(combined)\n",
        "print(\"result is:\", result)\n",
        "\n",
        "# Construct the URL with the keyword (this varies by website)\n",
        "base_url = \"https://www.google.com\"\n",
        "com_url = f\"{base_url}?q={result}\"\n",
        "print(com_url)\n",
        "# directly use google search\n",
        "from googlesearch import search\n",
        "query = result + \" filetype:csv\"\n",
        "for url in search(query, stop=5):  # stop=5, n=5 you can change this to get more or fewer results\n",
        "    print(url)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gz38CCa6-qkG",
        "outputId": "4ac7a1d8-5532-42fd-c83f-df0f1b5edab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your question is : Looking at the chart for 2019 year, what percentage of the primary energy \n",
            "Numbers: ['2019']\n",
            "Units: ['year']\n",
            "Keywords: ['chart', 'year', 'percentage', 'energy']\n",
            "result is: 2019+year+data+quads+petroleum+quads+transportation+quads+uses\n",
            "https://www.google.com?q=2019+year+data+quads+petroleum+quads+transportation+quads+uses\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from googlesearch import search\n",
        "\n",
        "filetypes = [\"csv\", \"xlsx\", \"json\", \"text\"]\n",
        "for ft in filetypes:\n",
        "    query = f\"filetype:{ft}\"\n",
        "    print(f\"\\nSearching for: {query}\")\n",
        "    for url in search(query, stop=1):\n",
        "        print(url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzlNg0j4VQuQ",
        "outputId": "06cdce5c-bebd-4ceb-dd0d-7f413a8c89a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Searching for: filetype:csv\n",
            "\n",
            "Searching for: filetype:xlsx\n",
            "\n",
            "Searching for: filetype:json\n",
            "\n",
            "Searching for: filetype:text\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import os\n",
        "from googlesearch import search # Import the search function\n",
        "\n",
        "# Assuming 'result' variable is defined in a previous cell\n",
        "# Perform the Google search to get the list of URLs\n",
        "query = result + \" filetype:csv\"\n",
        "urls = []\n",
        "print(f\"Searching for query: {query}\")\n",
        "for url in search(query, stop=2):\n",
        "    urls.append(url)\n",
        "\n",
        "print(f\"Found URLs: {urls}\") # Print the list of URLs\n",
        "\n",
        "# Filter for valid CSV URLs only\n",
        "urls = [line for line in urls if line.strip().endswith('.csv')]\n",
        "print(f\"Filtered URLs: {urls}\") # Print the filtered list of URLs\n",
        "\n",
        "\n",
        "# Create a target folder\n",
        "folder = 'energy'\n",
        "os.makedirs(folder, exist_ok=True)\n",
        "\n",
        "# Download and save\n",
        "for i, url in enumerate(urls, 1):\n",
        "    print(f\"Attempting to download from URL: {url}\") # Print the current URL in the loop\n",
        "    filename = os.path.join(folder, f'file_{i}.csv')\n",
        "    try:\n",
        "        response = requests.get(url)\n",
        "        response.raise_for_status()  # Raise an HTTPError for bad responses (4xx or 5xx)\n",
        "        with open(filename, 'wb') as f:\n",
        "            f.write(response.content)\n",
        "        print(f'Downloaded: {filename}')\n",
        "    except requests.exceptions.RequestException as e:\n",
        "        print(f\"Error downloading {url}: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYnpwIOSIaB9",
        "outputId": "7a8c53aa-77be-4f34-9a6d-57ede0009a52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Searching for query: 2019+year+data+quads+petroleum+quads+transportation+quads+uses filetype:csv\n",
            "Found URLs: []\n",
            "Filtered URLs: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install img2pdf\n",
        "!pip install pytesseract opencv-python pillow"
      ],
      "metadata": {
        "id": "m6CfDbupWCil"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import pytesseract\n",
        "from PIL import Image\n",
        "\n",
        "# Optional: Set path to tesseract executable if not in PATH\n",
        "# pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n",
        "\n",
        "# Load the image\n",
        "image_path = 'your_image.png'\n",
        "img = cv2.imread(image_path)\n",
        "\n",
        "# Convert to grayscale for better OCR accuracy\n",
        "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# Optional: Apply thresholding or denoising\n",
        "gray = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)[1]\n",
        "\n",
        "# Run OCR\n",
        "text = pytesseract.image_to_string(gray)\n",
        "\n",
        "# Print extracted text\n",
        "print(\"Extracted Text:\")\n",
        "print(text)"
      ],
      "metadata": {
        "id": "tKl1jn3RWLIz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dr_skxQ1_BRz"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "#integer\n",
        "x= 1.003480\n",
        "print(int(x))\n",
        "#rounded to the nth decimal\n",
        "print(round(x, 4))\n",
        "# significant figure\n",
        "def SF(x, n):\n",
        "    if x == 0:\n",
        "        return 0\n",
        "    return round(x, n - int(math.floor(math.log10(abs(x)))) - 1)\n",
        "print(SF(0.0123456, 3))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test by an example"
      ],
      "metadata": {
        "id": "ziUPpe8EUS-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vn9tQSIJ0li9",
        "outputId": "83d853e4-3833-4a35-b37a-820041472ab3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'age': 38.37379, 'temperature': 0.88}, {'age': 46.81203, 'temperature': 1.84}, {'age': 55.05624, 'temperature': 3.04}, {'age': 64.41511, 'temperature': 0.35}, {'age': 73.15077, 'temperature': -0.42}, {'age': 81.93244, 'temperature': 0.05}, {'age': 90.75925, 'temperature': 0.05}, {'age': 99.97031, 'temperature': -0.52}, {'age': 109.88879, 'temperature': 0.79}, {'age': 119.25888, 'temperature': -0.55}]\n"
          ]
        }
      ],
      "source": [
        "#download data from url\n",
        "#approach 1\n",
        "import requests\n",
        "\n",
        "url = \"https://prappleizer.github.io/Tutorials/MCMC/ice_core_data.txt\"\n",
        "response = requests.get(url)\n",
        "lines = response.text.strip().split('\\n')\n",
        "\n",
        "# Skip header if present and parse data\n",
        "data = []\n",
        "for line in lines:\n",
        "    if line.strip() and not line.startswith('#'):\n",
        "        # Split by any whitespace characters\n",
        "        values = line.split()\n",
        "        if len(values) > 4: # Ensure there are enough columns\n",
        "            try:\n",
        "                # Assuming age is the 3rd and temperature is the 5th value after splitting\n",
        "                A = float(values[2])\n",
        "                T = float(values[4])\n",
        "                data.append({'age': A, 'temperature': T})\n",
        "            except ValueError as e:\n",
        "                print(f\"Skipping line due to ValueError: {e} in line: {line}\")\n",
        "            except IndexError as e:\n",
        "                print(f\"Skipping line due to IndexError: {e} - not enough columns in line: {line}\")\n",
        "\n",
        "# Your data (as a Python list of dictionaries)\n",
        "print(data[:10]) # Print first 10 rows to verify"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Pandas DataFrame from the data\n",
        "import matplotlib.pyplot as plt\n",
        "df = pd.DataFrame(data)\n",
        "#Age Vs Temperature\n",
        "plt.figure(figsize=(8, 4)) # Set figure size\n",
        "plt.scatter(df['age'], df['temperature'], color='green', label='Data Points')\n",
        "plt.title('Age vs. Temperature')\n",
        "plt.xlabel('years')\n",
        "plt.ylabel('Temperature (C)') #plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "ex04L6o-U5B0",
        "outputId": "dffae233-76ea-408c-f26a-f7e8d7bb4d34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#approach 1\n",
        "!pip install mpmath\n",
        "from mpmath import mp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-gDlH_XqGvy",
        "outputId": "f98fb851-f5af-4b30-8423-8af5a8405258"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mpmath in /usr/local/lib/python3.12/dist-packages (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# an example\n",
        "mp.dps = 50  # decimal places\n",
        "x = mp.mpf('0.0000001')\n",
        "sqrt_x = mp.sqrt(x)\n",
        "print(\"√(0.0000001) =\", sqrt_x)\n",
        "print(\"(√(0.0000001))² =\", sqrt_x ** 2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llZ3IM0hoeyp",
        "outputId": "39516697-5da2-4d6f-abaf-fa3f4337892a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "√(0.0000001) = 0.00031622776601683793319988935444327185337195551393252\n",
            "(√(0.0000001))² = 0.0000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#download data from google search with a specific file type\n",
        "#approach 2\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "import spacy\n",
        "import re\n",
        "from googlesearch import search\n",
        "# Construct the URL with the keyword (this varies by website)\n",
        "#(sqrt(0.0000001))^2\n",
        "print(\"Your calculator is ready.\")\n",
        "base_url = \"https://www.google.com\"\n",
        "print(base_url)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QyHPYL4qf3C",
        "outputId": "7c0017ae-78ab-4764-ad60-4cea997b13f4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your calculator is ready.\n",
            "https://www.google.com\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# input math\n",
        "# input result\n",
        "print(\"Can you copy and paste your answer from Google ?\")\n",
        "calculator = input(\"Your result is ready: \")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onwie7BS3kyY",
        "outputId": "4e16eb31-e436-49fc-aadf-88eba3d42f71"
      },
      "execution_count": 20,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Can you copy and paste your answer from Google ?\n",
            "Your result is ready: 1e-7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wolframalpha\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPsoYlijNmAz",
        "outputId": "2ea2a7b8-b7ea-4c52-9a8a-4d0cf2842868"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wolframalpha\n",
            "  Downloading wolframalpha-5.1.3-py3-none-any.whl.metadata (2.4 kB)\n",
            "Collecting xmltodict (from wolframalpha)\n",
            "  Downloading xmltodict-1.0.0-py3-none-any.whl.metadata (14 kB)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.12/dist-packages (from wolframalpha) (10.8.0)\n",
            "Requirement already satisfied: jaraco.context in /usr/local/lib/python3.12/dist-packages (from wolframalpha) (6.0.1)\n",
            "Requirement already satisfied: httpx in /usr/local/lib/python3.12/dist-packages (from wolframalpha) (0.28.1)\n",
            "Requirement already satisfied: multidict in /usr/local/lib/python3.12/dist-packages (from wolframalpha) (6.6.4)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx->wolframalpha) (4.10.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx->wolframalpha) (2025.8.3)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx->wolframalpha) (1.0.9)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx->wolframalpha) (3.10)\n",
            "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx->wolframalpha) (0.16.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from anyio->httpx->wolframalpha) (1.3.1)\n",
            "Requirement already satisfied: typing_extensions>=4.5 in /usr/local/lib/python3.12/dist-packages (from anyio->httpx->wolframalpha) (4.15.0)\n",
            "Downloading wolframalpha-5.1.3-py3-none-any.whl (6.3 kB)\n",
            "Downloading xmltodict-1.0.0-py3-none-any.whl (13 kB)\n",
            "Installing collected packages: xmltodict, wolframalpha\n",
            "Successfully installed wolframalpha-5.1.3 xmltodict-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# full requestions L2YGGWUKYT\n",
        "# quick calculator G8TW52GE7R\n",
        "\n",
        "import wolframalpha\n",
        "\n",
        "# Use your actual App ID\n",
        "client = wolframalpha.Client(\"G8TW52GE7R\")\n",
        "\n",
        "# Query the engine\n",
        "res = client.query(\"sqrt(0.0000001)\")\n",
        "answer = next(res.results).text\n",
        "print(\"WolframAlpha result:\", answer)"
      ],
      "metadata": {
        "id": "F9zG10PySfws"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}